Anomaly detection in graph signals with canonical correlation analysis

Data from network-structured applications, like sensor networks or smart grids, often reside on complex supports. Specific graph signal processing tools are needed for effective utilization. Detecting anomalous events in graph signals holds relevance across various applications, ranging from monitoring energy and water supplies to environmental surveillance. In these problems, anomalies often activate localized groups of vertices in the graph. This paper introduces the Joint Graph-Regularized Wavelet CCA (JGWCCA) approach, which combines canonical correlation analysis (CCA) with dual-tree complex wavelet packet transform (DT-CWPT) and graph regularization. JGWCCA enables time-frequency analysis of graph signals while considering the underlying graph topology. Performance validation of JGWCCA is done through numerical simulations.